Automatic assessment of damage and repair costs in vehicles

ABSTRACT

A system and method are provided for automatically estimating a repair cost for a vehicle. A method includes: receiving, at a server computing device over an electronic network, one or more images of a damaged vehicle from a client computing device; performing image processing operations on each of the one or more images to detect external damage to a first set of parts of the vehicle; inferring internal damage to a second set of parts of the vehicle based on the detected external damage; and, calculating an estimated repair cost for the vehicle based on the detected external damage and inferred internal damage based on accessing a parts database that includes repair and labor costs for each part in the first and second sets of parts.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application is a continuation of U.S. application Ser. No.15/092,480, filed on Apr. 6, 2016, which is hereby incorporated byreference in its entirety.

BACKGROUND

Currently, after a vehicle has been damaged in a road accident orotherwise, the vehicle must be taken by the owner or a tow company to anauto repair shop for inspection. Inspection of the vehicle by a mechanicat the auto repair shop is required in order to assess which parts ofthe vehicle need to be repaired or replaced. An estimate is thengenerated based on the inspection. In some cases, when an insuranceclaim is filed, the estimate is forwarded to an insurance company toapprove the repairs before the repairs are made to the vehicle.

From end-to-end, the process of vehicle inspection, estimate generation,claim approval, and vehicle repair can be long and complex, involvingseveral parties including at least a customer, an auto repair shop, anda claim adjustor.

Accordingly, there is a need in the art for an improved system thatovercomes some of the drawbacks and limitations of conventionalapproaches.

SUMMARY

One embodiment of the disclosure includes a method for automaticallyestimating a repair cost for a vehicle, comprising: receiving, at aserver computing device over an electronic network, one or more imagesof a damaged vehicle from a client computing device; performingcomputerized image processing on each of the one or more images todetect damage to a set of parts of the vehicle; and, calculating anestimated repair cost for the vehicle based on the detected damage basedon accessing a parts database that includes repair costs. Additionally,in some embodiments, the server computing device may classify the lossas a total, medium, or small loss.

Another embodiment of the disclosure provides a method for automaticallyestimating a repair cost for a vehicle, comprising: receiving, at aserver computing device over an electronic network, one or more imagesof a damaged vehicle from a client computing device; performing imageprocessing operations on each of the one or more images to detectexternal damage to a first set of parts of the vehicle; inferringinternal damage to a second set of parts of the vehicle based on thedetected external damage; and, calculating an estimated repair cost forthe vehicle based on the detected external damage and inferred internaldamage based on accessing a parts database that includes repair andlabor costs for each part in the first and second sets of parts.Additionally, in some embodiments, the server computing device mayclassify the loss as a total, medium, or small loss.

Another embodiment of the disclosure provides a mobile device comprisinga camera, a display device, a processor, and a memory. The memory storesinstructions that, when executed by the processor, cause the mobiledevice to display prompts on the display device to capture damage to avehicle with the camera, by performing the steps of: receiving, in afirst user interface screen displayed on the display device, a selectionto initiate a new vehicle claim; displaying, in a second user interfacescreen displayed on the display device, graphical elements for selectionof a prompting interface for capture of images of damage to the vehicle;receiving selection of a graphical element corresponding to a promptinginterface; displaying one or more prompts on the display device tocapture a portion of the vehicle based on the selection of the graphicalelement corresponding to the prompting interface; causing the camera ofthe client device to capture an image of the vehicle based on displayingan outline of the portion of the vehicle; and, causing the image of thevehicle to be uploaded to a server for estimation of repair costs of thevehicle based on the image. Additionally, in some embodiments, theserver computing device may classify the loss as a total, medium, orsmall loss.

Another embodiment of the disclosure provides a system for estimating arepair cost for a vehicle. The system includes a client computingdevice, an electronic communications network, and a server computingdevice. The client computing device is configured to: display one ormore prompts on a display device of the client computing device tocapture a portion of the vehicle that has sustained damage, and capturean image of the vehicle based on displaying an outline of the portion ofthe vehicle. The electronic communications network is configured totransfer the image of the vehicle to a server computing device. Theserver computing device is configured to: receive the image over theelectronic communications network, perform image processing operationson the image to identify one or more damaged parts of the vehicle, andcalculate an estimated repair cost for the vehicle based on accessing aparts database that includes repair and labor costs for each part in theone or more damaged parts. Additionally, in some embodiments, the servercomputing device may classify the loss as a total, medium, or smallloss.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating a system in accordance with someexample embodiments of the disclosure.

FIG. 2 is a block diagram illustrating components of a client devicefrom the system illustrated in FIG. 1 according to some embodiments ofthe disclosure.

FIG. 3 is a block diagram illustrating components for a server from thesystem illustrated in FIG. 1 according to some embodiments of thedisclosure.

FIG. 4 is a flow diagram of method steps for automatically estimating arepair cost for a vehicle, according to one embodiment of thedisclosure.

FIG. 5 is a flow diagram of method steps for performing image processingon one or more images to detect external damage of the vehicle,according to one embodiment of the disclosure.

FIG. 6 is an example of an input image of a damaged vehicle, accordingto one embodiment of the disclosure.

FIG. 7 is an example of a color distribution of the input image fromFIG. 6, according to one embodiment of the disclosure.

FIG. 8 is an example of an edge distribution of the input image fromFIG. 6, according to one embodiment of the disclosure.

FIGS. 9A-9C illustrate a specular reflection removal process, accordingto one embodiment of the disclosure.

FIG. 10A is an example of a reference image of vehicle, according to oneembodiment of the disclosure.

FIG. 10B is an example of an input image of damage to a vehicle,according to one embodiment of the disclosure.

FIG. 11 is a conceptual diagram illustrating comparing a window from areference image to a corresponding window of an input image where nodamage is detected, according to one embodiment of the disclosure.

FIG. 12 is a conceptual diagram illustrating comparing a window from areference image to a corresponding window of an input image where damageis detected, according to one embodiment of the disclosure.

FIG. 13 is a conceptual diagram illustrating the various windows in aninput image where damage is detected, according to one embodiment of thedisclosure.

FIGS. 14-21 are screenshots of example interface screens of a vehicleclaims application on a client device, according to various embodimentsof the disclosure.

FIG. 22 is a screenshot of an example interface screen of an adjustercomputing device connected via a communications interface to a serverconfigured to automatically estimate repair costs, according to oneembodiment of the disclosure.

FIG. 23 is a flow diagram of method steps for a vehicle claimsapplication to prompt a client device to capture images of a damagedvehicle, according to one embodiment of the disclosure.

FIG. 24 is a block diagram illustrating a multi-stage design of amachine learning system, according to one embodiment.

FIG. 25 is a block diagram illustrating implementation of ConvolutionalNeural Networks (CNNs) to detect vehicle damage, according to oneembodiment.

FIG. 26 is a flow diagram of method steps for estimating vehicle damagefrom images of a damaged vehicle using a machine learning algorithm,according to one embodiment.

DETAILED DESCRIPTION

Embodiments of the disclosure provide systems and methods that applycomputer vision and image processing to images of a damaged vehicle todetermine which parts of the vehicle are damaged and estimate the costof repair or replacement, thus automating the damage assessment and costappraisal process. Additionally, in some embodiments, the servercomputing device may classify the loss as a total, medium, or smallloss.

The disclosed automatic vehicle damage assessment system is a softwaresystem that uses captured images of a damaged vehicle along withauxiliary information available from other sources to assess the damageand, optionally, to provide an appraisal of damage and estimate ofrepair costs. In some embodiments, the captured images comprise one ormore still images of the damaged vehicle and damaged areas. Theauxiliary data includes the vehicle's make, model, and year. In otherembodiments, the captured images include not only still images, but alsovideo, LIDAR imagery, and/or imagery from other modalities. In someembodiments, the auxiliary information includes additional informationavailable from insurance and vehicle registration records, publiclyavailable information for the vehicle make and model, vehicle data fromon-board sensors and installed devices, as well as information regardingthe state of the imaging device at the time of image capture, includinglocation information (e.g., GPS coordinates), orientation information(e.g., from gyroscopic sensors), and settings, among others.

The automatic vehicle damage assessment system is a first-of-its-kindsystem that leverages state-of-the-art computer vision and machinelearning technologies to partially or fully automate the auto claimssubmission and settlement process, thereby introducing efficiencies inauto insurance claims processing. The system can be expanded toadditional sensors and information sources as these become available onsmartphone devices including, for instance, stereo/depth sensingmodalities. Additionally, in some embodiments, the image capture processcan be interactive, with an application (“app”) installed on asmartphone or other client device that guides a user through the processof capturing images of the damaged vehicle.

In one example implementation, images (e.g., photos or videos) showingdamage to the vehicle are captured soon after the damage occurs. Theimages can be taken with a mobile phone and sent to a server by thevehicle owner or driver over a cellular or wireless network connection,either through a proprietary platform such a mobile application orthrough a web-based service. In some embodiments, an insurance companyfield inspector or adjustor visits the vehicle site, captures therequisite images and uploads them to the server, as is currently done insome jurisdictions or countries. In further embodiments, the images canbe captured by an auto repair shop to which the vehicle is taken afteran accident.

In embodiments where a mobile phone is used to collect the images,information about the camera's location from the mobile phone GPSsystem, the camera's orientation from the mobile phone's gyroscope andaccelerometer, the time at which the images are taken, and the camera'sresolution, image format, and related attributes can also be provided tothe server.

In embodiments where a telematics system is installed in the vehicle,the telematics system can provide information to the server about thevehicle's state at, prior to, and/or after the time of accident,velocity and acceleration profile of the vehicle, states of the airbagsand turn signals, and other relevant vehicle state data.

Certain “metadata” about the vehicle are also available and stored in adatabase accessible by the server. The metadata includes at least thevehicle make, model, and year. The metadata may optionally includeimages of the vehicle prior to the occurrence of damage.

According to embodiments of the disclosure, the assessment of damage andassociated repair costs relies upon image processing and machinelearning technologies. Computer vision techniques are used to firstclean the received images of unwanted artifacts, such as backgroundclutter and specular reflections, and then, to find the best matchingimage of a reference vehicle of the same make/model/year. The systemcompares the received images with the corresponding reference imagesalong several attributes, e.g., edge distribution, texture, and shape.Using a variety of computer vision techniques, the system recognizeswhere and how the received images depart from the reference images, andidentifies the corresponding part(s) and/or regions on the exterior ofthe vehicle that are damaged. The reference images can, in someembodiments, be derived from a commercial 3D model of a vehicle of thesame make and model, or from images of the same vehicle taken prior tothe occurrence of damage in the current claim, e.g., at the time ofpurchase of the auto policy.

Alternatively and in parallel, a deep learning system (e.g.,Convolutional Neural Network) is trained on a large number of images ofdamaged vehicles and corresponding information about damage, e.g., itsextent and location on the vehicle, which are available from aninsurance company's auto claims archives, in order to learn to assessdamage presented with input images for a new auto claim. Such a patternlearning method can predict damage to both the exterior and interior ofthe vehicle, as well as the associated repair costs. The assessment ofdamage to the exterior determined by the image processing system can beused as input to the pattern learning system in order to supplement andrefine the damage assessment. The current level of damage can becompared with the level of damage prior to filing of the current claim,as determined using image processing of prior images of the vehicle withthe same system.

A comprehensive damaged parts list is then generated to prepare anestimate of the cost required to repair the vehicle by looking up in aparts database for parts and labor cost. In the absence of such a partsdatabase, the system can be trained to predict the parts and labor costassociated with a damage assessment, since these are also available inthe archival data. In some embodiments, the regions and/or areas ofdamage on the exterior of the vehicle can also be identified.

In some embodiments, when additional information about the state of thevehicle at the time of the accident as well as of the camera used totake its images is available, the additional information can be used tofurther refine the system's predictive capabilities. In particular,knowing the location, velocity, and acceleration of the vehicle at thetime of accident allows an assessment of the extent of impact to thevehicle during the accident, which allows better estimation of theextent of damage to the exterior and interior of the vehicle. Knowingfurther whether airbags were deployed during the collision can be usefulfor determination of the extent of damage, including whether there mightbe a “total loss” of the vehicle. The orientation of the camera whenused to take images of the vehicle, as well as its location and time,can also assist the damage detection system in carrying out variousimage processing operations, as will become apparent during thediscussion below.

Advantageously, the automatic vehicle damage assessment systems andmethods provided herein allow an insurance company to increase itsefficiency of auto claims settlement processes. For example, automaticdetermination of “small value” claims can be settled rapidly withoutrequiring time and effort on the part of adjustors to adjudicate.Automatic determination of “total loss” claims can also lead to earlysettlement of the claim, resulting in substantial savings in vehiclestorage costs. Automatic verification of the damage appraisals sent byauto repair shops can supplant manual inspection of appraisals byadjustors and, in many cases, lead to efficiencies in adjustorinvolvement. Data aggregated across multiple claims and repair shops canalso help identify misleading appraisals and recurrent fraudulentactivity by repair shops. Early notification of the nature of damage canbe sent to partner repair shops, allowing them to schedule the resourcesneeded for repair early and more efficiently, reducing customer waittimes, and thereby, rental vehicle costs.

Also, customer satisfaction is enhanced in multiple ways. First, thesystem can rapidly identify the claims that have a small amount ofdamage and the claims that have such severe damage that the vehicle cannot be repaired and is a “total loss.” In at least these two cases, thecustomer can be sent a settlement check almost immediately upon filingof the claim, with minimal involvement of human adjustors. In othercases, where the damage falls between the two extremes and the vehiclehas to be taken to an auto repair shop, appraisal of the damage by theshop can be automatically checked by the system, leading to detection ofpotentially fraudulent claims, again with minimal requirement of a humanadjustors' time and effort.

Turning now to the figures, FIG. 1 is a block diagram of a system 100 inaccordance with certain embodiments of the disclosure. The system 100includes a server or cluster of servers 102, one or more client deviceslabeled 104-1 through 104-n, an adjuster computing device 106, severalnetwork connections linking client devices 104-1 through 104-n toserver(s) 102 including the network connections labeled 108-1 through108-m, one or more databases 110, and a network connection 112 betweenthe server(s) 102 and the adjuster computing device 106.

The client device or plurality of client devices 104 and the adjustercomputing device 106 can be any type of communication devices thatsupport network communication, including a telephone, a mobile phone, asmart phone, a personal computer, a laptop computer, a smart watch, apersonal digital assistant (PDA), a wearable or embedded digitaldevice(s), a network-connected vehicle, etc. In some embodiments, theclient devices 104 and adjuster computing device 106 can supportmultiple types of networks. For example, the client devices 104 and theadjuster computing device 106 may have wired or wireless networkconnectivity using IP (Internet Protocol) or may have mobile networkconnectivity allowing over cellular and data networks.

The various networks 108, 112 may take the form of multiple networktopologies. For example, network 108 comprises wireless and/or wirednetworks. Networks 108 link the server 102 and the client devices 104.Networks 108 include infrastructure that support the links necessary fordata communication between at least one client device 104 and server102. Networks 108 may include a cell tower, base station, and switchingnetwork.

As described in greater detail herein, client devices 104 are used tocapture one or more images of a damaged vehicle. The images aretransmitted over a network connection 108 to a server 102. The server102 processes the images to estimate damage and repair costs. Theestimates are transmitted over network connection 112 to the adjustcomputer device 106 for approval or adjustment.

FIG. 2 is a block diagram of basic functional components for a clientdevice 104 according to some aspects of the disclosure. In theillustrated embodiment of FIG. 2, the client device 104 includes one ormore processors 202, memory 204, network interfaces 206, storage devices208, power source 210, one or more output devices 212, one or more inputdevices 214, and software modules—operating system 216 and a vehicleclaims application 218—stored in memory 204. The software modules areprovided as being contained in memory 204, but in certain embodiments,the software modules are contained in storage devices 208 or acombination of memory 204 and storage devices 208. Each of thecomponents including the processor 202, memory 204, network interfaces206, storage devices 208, power source 210, output devices 212, inputdevices 214, operating system 216, the network monitor 218, and the datacollector 220 is interconnected physically, communicatively, and/oroperatively for inter-component communications.

As illustrated, processor 202 is configured to implement functionalityand/or process instructions for execution within client device 104. Forexample, processor 202 executes instructions stored in memory 204 orinstructions stored on a storage device 208. Memory 204, which may be anon-transient, computer-readable storage medium, is configured to storeinformation within client device 104 during operation. In someembodiments, memory 204 includes a temporary memory, an area forinformation not to be maintained when the client device 104 is turnedoff. Examples of such temporary memory include volatile memories such asrandom access memories (RAM), dynamic random access memories (DRAM), andstatic random access memories (SRAM). Memory 204 also maintains programinstructions for execution by the processor 202.

Storage device 208 also includes one or more non-transientcomputer-readable storage media. The storage device 208 is generallyconfigured to store larger amounts of information than memory 204. Thestorage device 208 may further be configured for long-term storage ofinformation. In some embodiments, the storage device 208 includesnon-volatile storage elements. Non-limiting examples of non-volatilestorage elements include magnetic hard discs, optical discs, floppydiscs, flash memories, or forms of electrically programmable memories(EPROM) or electrically erasable and programmable (EEPROM) memories.

Client device 104 uses network interface 206 to communicate withexternal devices or server(s) 102 via one or more networks 108 (see FIG.1), and other types of networks through which a communication with theclient device 104 may be established. Network interface 206 may be anetwork interface card, such as an Ethernet card, an opticaltransceiver, a radio frequency transceiver, or any other type of devicethat can send and receive information. Other non-limiting examples ofnetwork interfaces include Bluetooth®, 3G and Wi-Fi radios in clientcomputing devices, and Universal Serial Bus (USB).

Client device 104 includes one or more power sources 210 to providepower to the device. Non-limiting examples of power source 210 includesingle-use power sources, rechargeable power sources, and/or powersources developed from nickel-cadmium, lithium-ion, or other suitablematerial.

One or more output devices 212 are also included in client device 104.Output devices 212 are configured to provide output to a user usingtactile, audio, and/or video stimuli. Output device 212 may include adisplay screen (part of the presence-sensitive screen), a sound card, avideo graphics adapter card, or any other type of device for convertinga signal into an appropriate form understandable to humans or machines.Additional examples of output device 212 include a speaker such asheadphones, a cathode ray tube (CRT) monitor, a liquid crystal display(LCD), or any other type of device that can generate intelligible outputto a user.

The client device 104 includes one or more input devices 214. Inputdevices 214 are configured to receive input from a user or a surroundingenvironment of the user through tactile, audio, and/or video feedback.Non-limiting examples of input device 214 include a photo and videocamera, presence-sensitive screen, a mouse, a keyboard, a voiceresponsive system, microphone or any other type of input device. In someexamples, a presence-sensitive screen includes a touch-sensitive screen.

The client device 104 includes an operating system 216. The operatingsystem 216 controls operations of the components of the client device104. For example, the operating system 216 facilitates the interactionof the processor(s) 202, memory 204, network interface 206, storagedevice(s) 208, input device 214, output device 212, and power source210.

As described in greater detail herein, the client device 104 usesvehicle claims application 218 to capture one or more images of adamaged vehicle. In some embodiments, the vehicle claims application 218may guide a user of the client device 104 as to which views should becaptured. In some embodiments, the vehicle claims application 218 mayinterface with and receive inputs from a GPS transceiver and/oraccelerometer.

Server(s) 102 is at least one computing machine that can automaticallycalculate an estimate for vehicle repair costs based on images providedfrom a client device 104. The server 102 has access to one or moredatabases 110 and other facilities that enable the features describedherein.

According to certain embodiments, similar elements shown in FIG. 2 to beincluded in the client device 104 can also be included in the adjustercomputing device 106. The adjuster computing device 106 may furtherinclude software stored in a memory and executed by a processor toreview and adjust repair cost estimates generated by the server 102.

Turning to FIG. 3, a block diagram is shown illustrating components fora server 102, according to certain aspects of the disclosure. Server 102includes one or more processors 302, memory 304, network interface(s)306, storage device(s) 308, and software modules—image processing engine310, damage estimation engine 312, and database query and edit engine314—stored in memory 304. The software modules are provided as beingstored in memory 304, but in certain embodiments, the software modulesare stored in storage devices 308 or a combination of memory 304 andstorage devices 308. In certain embodiments, each of the componentsincluding the processor(s) 302, memory 304, network interface(s) 306,storage device(s) 308, media manager 310, connection service router 312,data organizer 314, and database editor 316 are interconnectedphysically, communicatively, and/or operatively for inter-componentcommunications.

Processor(s) 302, analogous to processor(s) 202 in client device 104, isconfigured to implement functionality and/or process instructions forexecution within the server 102. For example, processor(s) 302 executesinstructions stored in memory 304 or instructions stored on storagedevices 308. Memory 304, which may be a non-transient, computer-readablestorage medium, is configured to store information within server 102during operation. In some embodiments, memory 304 includes a temporarymemory, i.e., an area for information not to be maintained when theserver 102 is turned off. Examples of such temporary memory includevolatile memories such as random access memories (RAM), dynamic randomaccess memories (DRAM), and static random access memories (SRAM). Memory304 also maintains program instructions for execution by processor(s)302.

Server 102 uses network interface(s) 306 to communicate with externaldevices via one or more networks depicted as network 108 and network 112in FIG. 1. Such networks may also include one or more wireless networks,wired networks, fiber optics networks, and other types of networksthrough which communication between the server 102 and an externaldevice may be established. Network interface(s) 306 may be a networkinterface card, such as an Ethernet card, an optical transceiver, aradio frequency transceiver, or any other type of device that can sendand receive information.

Storage devices 308 in server 102 also include one or more non-transientcomputer-readable storage media. Storage devices 308 are generallyconfigured to store larger amounts of information than memory 304.Storage devices 308 may further be configured for long-term storage ofinformation. In some examples, storage devices 304 include non-volatilestorage elements. Non-limiting examples of non-volatile storage elementsinclude magnetic hard discs, optical discs, floppy discs, flashmemories, resistive memories, or forms of electrically programmablememories (EPROM) or electrically erasable and programmable (EEPROM)memories.

Server 102 further includes instructions that implement an imageprocessing engine 310 that receives images of a damaged vehicle from oneor more client devices 104 and performs image processing on the images.Server 102 further includes instructions that implement a damageestimation engine 312 that receives the images processed by the imageprocessing engine 310 and, in conjunction with a database query and editengine 314 that has access to a database 110 storing parts and laborcosts, calculates an estimate for repair or replacement of the damagedvehicle.

FIG. 4 is a flow diagram of method steps for automatically estimating arepair cost for a vehicle, according to one embodiment of thedisclosure. As shown, the method 400 begins at step 402, where a server,such as server 102, receives one or more images of a damaged vehiclefrom a client device. In some embodiments, the images may includeadditional metadata, such as GPS location.

At step 404, the server performs image processing on the one or moreimages to detect external damage of the vehicle. As described in greaterdetail in FIG. 5, performing image processing on the one or more imagesincludes: image cleaning to remove artifacts such as background andspecular reflection, image alignment to an undamaged version of thevehicle, image segmentation into vehicle parts, and damage assessment,including edge distribution, texture comparison, and spatial correlationdetection.

In some embodiments, if the camera's position and orientation are knownfor a given image, this information can help with the image alignmentstep by providing a rough estimation of the two-dimensional projectionrequired to produce the reference image. In some embodiments, if anoutline of the vehicle or the part whose image is intended to be takenis placed within the camera view for the image taker to align the imageto, then the accuracy and efficiency of the background removal procedurecan be substantially improved. In some embodiments, if the state of thevehicle just prior to and during the accident can be obtained from atelematics system, then a dynamic model of the vehicle movement can beconstructed, the forces each part of the vehicle is subject during anyimpact estimated, and therefore, the amount of its distortion includingdisplacement in depth assessed.

At step 406, the server infers internal damage to the vehicle fromdetected external damage. Once the externally damaged parts areidentified, the server can look up in a database which internal partsare also likely to be replaced based on the set of damaged externalparts. This inference can be based on historical models for whichinternal parts needed to be replaced given certain external damage inprior repairs.

At step 408, the server calculates an estimates repair cost for thevehicle based on the detected external damage and inferred internaldamage. The server accesses one or more databases of parts and laborcost for each external and internal part that is estimated to needrepair or replacement. The estimate can be provided to an insuranceclaim adjuster for review, adjustment, and approval.

FIG. 5 is a flow diagram of method steps for performing image processingon one or more images to detect external damage of the vehicle,according to one embodiment of the disclosure. In some embodiments, thesteps of the method shown in FIG. 5 provided an implementation of step404 from FIG. 4.

As shown in FIG. 5, the method begins at step 502, where a serverperforms image cleaning to remove background and specular reflection. Asstep 504, the server performs image alignment to align the image to areference image. As step 506, the server performs image segmentation todivide the vehicle into vehicle parts. As step 508, the server performsdamage assessment based on (a) edge distribution, (b) texture, and (c)spatial correlation.

Each of the one or more images provided to the server from the clientdevice is processed separately according to the method shown in FIG. 5.A detailed explanation of performing each of steps 502, 504, 506, 508 isprovided below.

At step 502 (i.e., image cleaning), each image is cleaned to removebackground and specular reflections due to incident light.

In a first embodiment of implementing step 502, background removal canbe performed with image segmentation using Conditional Random Fields(CRF) realized as Recurrent Neural Networks (RNN).

In the technique, the image is modeled as a conditional random field.Each pixel in the image is regarded as a node in a mathematical graph.Two nodes are connected by an edge in the graph if their correspondingpixels are neighbors. Each node is assigned a binary label according towhether the corresponding pixel is deemed to belong to the foreground(i.e., the vehicle) or the background. The binary label can be taken tobe 1 for the foreground and −1 for the background. Once all of thepixels in the image have been assigned a binary label properly, thepixels labeled as background can be removed achieving segmentation ofthe background.

In order to find the node binary labels, two functions are used. Thevalue of the function ψ_(n)(x_(i)) denotes the “cost” of the node Itaking the value x_(i). The value of the function ψ_(p)(x_(i), x_(j))denotes the “cost” of the neighboring nodes I and J taking the valuex_(i) and x_(j), respectively. Using these functions, the followingenergy function for an image X can be defined:

${{E(x)} = {{\sum\limits_{i}{\psi_{u}\left( x_{i} \right)}} + {\sum\limits_{i < j}{\psi_{p}\left( {x_{i},x_{j}} \right)}}}},$

The probability of an image is defined to be e^((−E(X))) suitablynormalized. The task is to learn the parameters of two functions ψ_(u)and ψ_(p) from a large database of real images so that theirprobabilities are maximized, or equivalently, their energies areminimized.

The unary function ψ_(u) can be learned using a convolutional neuralnetwork (CNN). The network is repeatedly shown a succession of trainingimages in which each pixel has been correctly labeled asforeground/background. Starting with random weights, the weights areadjusted using a standard backpropagation algorithm in order to predictthe labeling correctly.

The function ψ_(p) can be modeled as:

${{\psi_{p}\left( {x_{i},x_{j}} \right)} = {{\mu\left( {x_{i},x_{j}} \right)} = {\sum\limits_{m = 1}^{M}\;{w^{(m)}{k_{G}^{(m)}\left( {f_{i},f_{j}} \right)}}}}},$where k_(G) is a Gaussian kernel, f_(i) are features derived from theimage and μ is a label-compatibility function. ψ_(p) can be learnedusing the following algorithm, in the which the steps can be implementedas a CNN:

$\left. {Q_{i}(l)}\leftarrow{\frac{1}{Z_{1}}{\exp\left( {U_{i}(l)} \right)}\mspace{14mu}{for}\mspace{14mu}{all}\mspace{14mu} i} \right.$

 Initialization while not converged do  $\left. {{\overset{\sim}{Q}}_{i}^{(m)}(l)}\leftarrow{\sum\limits_{j \neq i}{{k^{(m)}\left( {f_{i},f_{j}} \right)}{Q_{j}(l)}\mspace{14mu}{for}\mspace{14mu}{all}\mspace{14mu} m}} \right.$

 Message Passing  $\left. {{\overset{ˇ}{Q}}_{i}(l)}\leftarrow{\sum\limits_{m}{w^{(m)}{{\overset{\sim}{Q}}_{i}^{(m)}(l)}}} \right.$

 Weighting Filter Outputs  $\left. {{\hat{Q}}_{i}(l)}\leftarrow{\sum\limits_{l^{\prime} \in \mathcal{L}}{{\mu\left( {l,l^{\prime}} \right)}{{\overset{ˇ}{Q}}_{i}\left( l^{\prime} \right)}}} \right.$

 Compatibility Transform  Q̆_(i)(l) ← U_(i)(l) − {circumflex over(Q)}_(i)(l)

 Adding Unary Potentials  $\left. Q_{i}\leftarrow{\frac{1}{Z_{1}}{\exp\left( {{\overset{˘}{Q}}_{i}(l)} \right)}} \right.$

 Normalizing end while

In a second embodiment of implementing step 502, for background removal,an “active contour” technique can be used to produce a curve called acontour that lies as close to the boundary of the vehicle in the imageas possible. The contour serves to separate the vehicle from itsbackground. Anything outside the curve is then removed (e.g., byconverting that part of image to black or white, depending on the colorof the vehicle).

In one embodiment, the active contour technique starts with auser-supplied initial contour (i.e., closed curve) containing thevehicle within the photo and defining an energy function of the contourthat takes its minimum value when the contrast in color and intensityacross the contour is maximum, which is assumed to be the indicator ofthe vehicle boundary. For example, the user-supplied initial contour canbe provided by an insurance adjuster utilizing a computing device incommunication with the server.

The initial contour is evolved along the gradient of the energy functionuntil the gradient becomes zero, i.e., when the energy function hasachieved an extremal value. An energy function E is defined so that itsminimum should correspond to a good segmentation of the image intoforeground and background:E(α,k,θ,z)=U(α,k,θ,z)+V(+,z)where the U( ) evaluates the color distribution and V( ) evaluates theedge or gradient distribution, z=(Z₁, . . . , Z_(n), . . . , Z_(N)) isthe image thought of as an RGB-valued array, and α ∈ {0,1} is the binarysegmentation map, with 0 for background and 1 for foreground. For eachassignment of values of a to the pixels the corresponding energy can becomputed.

In one embodiment, the color term U is a Gaussian Mixture Model (GMM)defined as follows:

${{U\left( {\underset{\_}{\alpha},k,\underset{\_}{\theta},z} \right)} = {\sum\limits_{n}{D\left( {\alpha_{n},k_{n},\underset{\_}{\theta},z_{n}} \right)}}},{{D\left( {\alpha_{n},k_{n},\underset{\_}{\theta},z_{n}} \right)} = {{{- \log}\mspace{14mu}{p\left( {{z_{n}❘\alpha_{n}},k_{n},\underset{\_}{\theta}} \right)}} - {\log\mspace{14mu}{\pi\left( {\alpha_{n},k_{n}} \right)}}}}$where p( ) is a Gaussian probability distribution and π( ) is themixture weighting coefficient, so that:D(α_(n) ,k _(n) ,θ,z _(n))=− log π(α_(n) k _(n)+½ log det Σ(α_(n) ,k_(n))+½[z _(n)−μ(α_(n) ,k _(n))]^(T)Σ(α_(n) ,k _(n))⁻¹[z _(n)−μ(α_(n) ,k_(n))].

Therefore, the color modeling parameters are:θ={π(α,k),μ(α,k),Σ(α,k),α=0,1,k=1, . . . K}

In one embodiment, the edge term Vis defined as:

${V\left( {\underset{\_}{\alpha},z} \right)} = {{\gamma{\sum\limits_{{({m,n})} \in C}{\left\lbrack {\alpha_{n} \neq \alpha_{m}} \right\rbrack\exp}}} - {\beta{{z_{m} - z_{n}}}^{2}}}$where [ ] denotes the indicator function taking values 0 or 1, C is theset of pairs of neighboring pixels, and other two scalars are inputparameters (determined by experiments).

In one embodiment, a user, such as a claims adjuster, initializes theprocess by supplying an initial background for the image. For example,initialize a=0 for pixels in background and a=1 for pixels inforeground. An iterative process is then performed as follows:

Iterative Minimisation

-   -   1. Assign GMM components to pixels:

$k_{n}\mspace{14mu}\text{:=}\mspace{14mu}\arg\mspace{14mu}{\min\limits_{k_{n}}\mspace{14mu}{{D_{n}\left( {\alpha_{n},k_{n},\theta,z_{n}} \right)}.}}$

-   -   2. Learn GMM parameters from data z:

$\underset{\_}{\theta}\mspace{14mu}\text{:=}\mspace{14mu}\arg\mspace{14mu}{\min\limits_{\underset{\_}{\theta}}\mspace{14mu}{U\left( {\underset{\_}{\alpha},k,\underset{\_}{\theta},z} \right)}}$

-   -   3. Estimate segmentation: use min cut to solve:

$\min\limits_{\{{{\alpha_{n}\text{:}\mspace{14mu} n} \in T_{U}}\}}{\min\limits_{k}\mspace{14mu}{{E\left( {\underset{\_}{\alpha},k,\underset{\_}{\theta},z} \right)}.}}$

4. Repeat from step 1, until convergence.

However, the choice of the initial contour is critical, and the activecontour technique itself does not specify how to choose an appropriateinitial contour. Since the location of the vehicle within the image isnot known, one might put the initial contour at or close to the boundaryof the photo in order to ensure that the vehicle is always containedwithin it. However, this often results in other objects being includedin the final result of the background removal process.

Some embodiments of the disclosure improve upon existing techniques byusing a Deformable Part Model (DPM) to obtain the initial contour. DPMis a machine learning model usually used to recognize objects made ofmoveable parts. At a high level, DPM can be characterized by stronglow-level features based on histograms of oriented gradient (HOG) thatis globally invariant to illumination and locally invariant totranslation and rotation, efficient matching algorithms for deformablepart-based models, and discriminative learning with latent variables.After training on a large database of vehicles in various orientations,the DPM learns to put a bounding box around the vehicle in the photo.This bounding box can then serve as the initial contour.

Even with a much better choice of initial contour, the backgroundremoval process is not always perfect due to the presence of damage andspecular reflections. For example, sometimes only part of the vehicle isretained. To solve this problem, embodiments of the disclosure provide asolution by first segmenting the image into “super-pixels.” Asuper-pixel algorithm group pixels into perceptually meaningful atomicregions. Therefore, if parts of the atomic region are missing,embodiments of the disclosure can recover them by checking atomic regionintegrity. In one implementation, k-means clustering can be used togenerate super-pixels. The similarity measurement for pixels isdetermined by the Euclidean distance in LAB space (i.e., a type of colorspace).

In view of the above, embodiments of the disclosure provide novel imageprocessing techniques to achieve excellent performance on backgroundremoval.

FIG. 6 is an example of an input image of a damaged vehicle, accordingto one embodiment of the disclosure. FIG. 7 is an example of a colordistribution of the input image from FIG. 6, according to one embodimentof the disclosure. FIG. 8 is an example of an edge distribution of theinput image from FIG. 6, according to one embodiment of the disclosure.

In some embodiments, specular reflection removal is also used to removespecular reflections on the metallic surfaces of the vehicle. Reflectionremoval is performed by a combination of two techniques. In a firsttechnique, embodiments of the disclosure apply a high-pass spatialfilter to the image. Applying a high-pass filter assumes that specularreflections are low spatial frequency additive components of the imageintensity. The frequency threshold of the filter can be determinedempirically.

In a second technique, embodiments of the disclosure apply a method thatexamines each pixel of the image. Pixels whose intensity values havereached a maximum in either of the three color channels (i.e., red (R),green (G), and blue (B)) are assumed to be “saturated” due to strongincident light, and are re-assigned color values of nearby pixels thatare of the same color, but unsaturated. This technique of finding theappropriate nearest unsaturated pixel is novel relative to conventionalapproaches. Among the nearest such pixels, embodiments of the disclosurechoose the ones that lie on the same part of the vehicle as thesaturated pixel in question, which ensures that they have the same truecolor, and use the mean ratios between the R, G and B values of theunsaturated pixels to correct the RGB values of the saturated pixelbecause despite considerable lighting variations, the ratios aresupposed to remain invariant.

FIGS. 9A-9C illustrate a specular reflection removal process, accordingto one embodiment of the disclosure. FIG. 9A illustrates an input imageof a damaged vehicle with the background removed. FIG. 9B illustratesthe low frequency components of a damaged vehicle in FIG. 9A. FIG. 9Cillustrates a reflection-removed version of the vehicle in FIG. 9A, withlow frequency components removed and color corrected to remove saturatedpixels.

Referring back to FIG. 5, at step 504 (i.e., image alignment), areference image is found for the same vehicle type that is taken fromthe same camera position and orientation as the damaged vehicle image.Once the input image is aligned to a reference image, the server is ableto overlay the two images on top of each other so that the vehicleboundaries within them more or less coincide. This is called imagealignment.

In one embodiment, the server starts with a three-dimensional model ofthe vehicle and finds a two-dimensional projection of thethree-dimensional model that best matches the cleaned image of thedamaged vehicle. The match is determined in two stages.

In a first stage, “mutual information” between the input image and atemplate is determined. Mutual information is a statistical measure ofsimilarity of the spatial distributions of the normalized intensities inthe two images. In order to find the best match, a sequence of“similarity transformations” are applied to the three-dimensional modeland mutual information of the resulting two-dimensional projections iscomputed until the ones with the maximum mutual information is obtained.The top few templates with the highest mutual information with thedamaged image are kept. The top one turns out to not necessarily be thecorrect template because of the inability of mutual information tosometimes distinguish between front/back and left/right sides of thevehicle.

In a second stage, another statistical measure “cross-correlation” isused to choose among the top few selected templates. Cross-correlationmeasures different similarity properties of the two images, andtherefore, is able to break the tie among the front/back or left/rightsides to come up with the correct template.

According to some embodiments, three-dimensional models of variousvehicles can be purchased from commercial providers of three-dimensionalrenderings of objects, including the vehicle manufacturers themselves.Alternatively, the three-dimensional models can be constructed from acollection of two-dimensional images of the vehicle taken prior tooccurrence of damage. In one implementation of constructing thethree-dimensional model from two dimensional images, first a number offeature points of a certain type, e.g., scale-invariant featuretransform (SIFT) are computed in each two-dimensional image. Next,correspondences between similar feature points across images areestablished. These correspondences determine the mutual geometricalrelationships of the two-dimensional images in three-dimensional spaceusing mathematical formulas. These relationships allow us to “stitch”the two-dimensional images together into a three-dimensional model ofthe vehicle.

At step 506 (i.e., image segmentation), the cleaned image of the damagedvehicle is segmented into vehicle parts, i.e., the boundaries of thevehicle parts are determined and drawn. Segmentation is carried out inorder to assess damage on a part-by-part basis, which makes for morerobust damage assessment.

First, the reference image is itself segmented. This can be done easily,since commercial three-dimensional models usually come equipped withsegmentation into its component parts.

Next, an attempt is made to locate each part present in the referenceimage within the damaged input image. The initial position of the partis located by simply overlaying the reference image onto the damagedimage and projecting the boundary of the part on to the damaged image.This is then shrunk uniformly in order to arrive at an initial contour,which is then evolved along the gradient of an energy function in amanner analogous to the method of background removal until the energyfunction reaches its minimum, which is regarded as occurring when thecontour coincides with the part boundary, where there is a locally largedifference in intensity across the contour. In order to prevent one partfrom “leaking” into another, some embodiments use the part template todefine the zone within which the evolving part in the damaged image mustbe confined to. Some embodiments also apply consistency checks acrossdifferent parts found to make sure that they do not overlap or arecompletely absent.

In some embodiments, level set methods can be used to perform imagesegmentation. In level set methods, a contour of interest is embedded asthe zero level set of a level-set function (LSF) ϕ, where ϕ is afunction of time t. Initially at t=0, some embodiments choose a seedcontour inside the object of interest. For segmentation applications,the energy function is an edge-based geometric active model. Thefunction is defined such that its minimum is reached (therefore, stopevolving) as soon as the zero level set touches the object boundary. Inone implementation, the energy function is defined as:ε_(ϵ)(ϕ)=μƒ₁₀₆ p(|∇ϕ|)dx+λƒ_(Ω) gσ _(ϵ)(ϕ)|∇φ|dx+αƒ_(Ω)gH_(ϵ)(−ϕ)dx.

The first term in the energy function ε above is the regularizationterm. The regularization function is defined as:

${p_{2}(s)} = \left\{ \begin{matrix}{{\frac{1}{\left( {2\pi} \right)^{2}}\left( {1 - {\cos\left( {2\pi\; s} \right)}} \right)},} & {{{if}\mspace{14mu} s} \leq 1} \\{{{\frac{1}{2}\left( {s - 1} \right)^{2}},}\mspace{110mu}} & {{{if}\mspace{14mu} s} \geq 1.}\end{matrix} \right.$

Let I be an image on a domain Ω, and the edge indicator function g isdefined as:

$g\overset{\Delta}{=}\frac{1}{1 + {{{\nabla G_{\sigma}}*I}}^{2}}$where G_(σ)is a Gaussian smoothing kernel. In some embodiments, theGaussian kernel is replaced with a non-linear filter that is called abilateral filter. The filter weights depend not only on Euclideandistance of pixels, but also on the radiometric difference, e.g., pixelgrayscale intensity. This preserves sharp edges by systematicallylooping through each pixel and adjusting weights to the adjacent pixelsaccordingly.

The second term in the energy function ε above is a line integral of thefunction g along the zero level set of energy function. The otherintegral part is defined as:

${\delta_{ɛ}(x)} = \left\{ \begin{matrix}{{\frac{1}{2ɛ}\left\lbrack {1 + {\cos\left( \frac{\pi\; x}{ɛ} \right)}} \right\rbrack},} & {{x} \leq ɛ} \\{{0,}\mspace{166mu}} & {{x} > ɛ}\end{matrix} \right.$

The third term in the energy function ε above is to speed up theevolution. The function is defined as:

${H_{\epsilon}(x)} = \left\{ \begin{matrix}{{\frac{1}{2}\left( {1 + \frac{x}{ɛ} + {\frac{1}{\pi}{\sin\left( \frac{\pi\; x}{ɛ} \right)}}} \right)},} & {{{x} \leq ɛ}\mspace{25mu}} \\{{1,}\mspace{225mu}} & {{x > ɛ}\mspace{34mu}} \\{{0,}\mspace{225mu}} & {x < {- {ɛ.}}}\end{matrix} \right.$

The energy function ε is minimized by solving the gradient flow:

$\frac{\partial\phi}{\partial t} = {{\mu\;{{div}\left( {{d_{p}\left( {{\nabla\phi}} \right)}{\nabla\phi}} \right)}} + {{{\lambda\delta}_{ɛ}(\phi)}{{div}\left( {g\frac{\nabla\phi}{{\nabla\phi}}} \right)}} + {\alpha\; g\;{\delta_{ɛ}(\phi)}}}$

At the end of the image segmentation step, each vehicle part present inthe image of the damaged vehicle is separately delineated.

At step 508 (i.e., damage assessment), the segmented image of thedamaged vehicle and the corresponding reference image are compared forsignificant differences that are attributable to damage to the vehicle.The reference image can be the image of the same vehicle prior tooccurrence of damage or of a commercial 3D model. In order to localizedamage, each image is divided into small rectangular regions called“windows” in such a manner that the window boundaries in the twocoincide. Within each window the images are compared for edgedistribution, texture, and spatial correlation.

For edge distribution, embodiments of the disclosure follow theobservation that an undamaged image of a vehicle consists primarily ofedges (i.e., straight line segments arising from significant andconsistent changes in color and intensity) that are regular in structureand orientation, which are disturbed in the portions where damage hasoccurred. Embodiments of the disclosure first find edges in the twoimages using a standard edge finding algorithm, and then compute thedistributions of the length and orientations of edges in each window.The distance between the distributions within a window is then computed(using entropy or Kullback-Leibler divergence, for example). If a windowexceeds a threshold that is empirically determined, the window maycontain damage.

According to one implementation of a method for edge map comparison, themethod first computes the edges of each parts using Canny edge detector.Second, the method detects straight lines on the edge maps from all thepossible orientations. Then, the method calculates the probability ofeach orientation having a straight line. Finally, the method checks theentropy difference between template and damage car based on theprobability distribution obtained from last step

Regarding texture comparison, texture is a way to characterize patternsof intensity changes across an image. In an image of a clean vehicle,each part of the vehicle has a specific texture. When the part isdamaged, the part's texture often changes also. Embodiments of thedisclosure compute measures of texture such as entropy, derived fromlocally-oriented intensity gradients for both images in each window andtake their difference. If the sum of the magnitudes of differencesexceeds an empirically established threshold, the window is regarded aspossibly containing damage.

According to one implementation of a method for texture differencedetection, first image pairs are transformed to grayscale image. Then,the method computes the co-occurrence matrix for each part. Finally, themethod checks the homogeneity difference based on the co-occurrencematrix.

For image correlation, in one the auto-correlation and cross-correlationdifference Metric is computed as follows:Metric=∫_(−∞) ^(x)∫_(−∞) ^(∞∫) _(−∞) ^(∞∫) _(−∞) ^(∞)f(x−α,y−b){g(x,y)−f(x,y)}dxdydadb

In another embodiment, another way to capture differences betweenpatterns of intensity in the damaged and reference images is via spatialcorrelation, or equivalently, spatial frequency. Some embodiments,compute the spatial frequency components present in the two images ineach window. Just as with edges and texture, if they differ appreciably,the window is regarded as a candidate for containing damage.

FIG. 10A is an example of a reference image of vehicle, according to oneembodiment of the disclosure. FIG. 10B is an example of an input imageof damage to a vehicle, according to one embodiment of the disclosure.

As described above, the reference image and input image are divided intosegments or “windows,” that are compared to one another on the basis ofedge distribution, texture, and spatial correlation. These measures ofdifference between the two images are then combined together for thefinal determination of damage within each window.

In some embodiments, if more than one measure contributes to theexistence of damage, the system asserts that damage within the windowexists. The exact proportion of weight assigned to each measure can bedetermined empirically through testing on real images. The weights canalso be determined through supervised machine learning on auto claimsdata.

FIG. 11 is a conceptual diagram illustrating comparing a window from areference image to a corresponding window of an input image where nodamage is detected, according to one embodiment of the disclosure. Asshown, for each of edge distribution, texture, and spatial correlation,the difference between the window from the reference image and thewindow from the input image does not exceed the respective threshold.

FIG. 12 is a conceptual diagram illustrating comparing a window from areference image to a corresponding window of an input image where damageis detected, according to one embodiment of the disclosure. As shown,damage is detected since the threshold different from edge distribution,texture, and spatial correlation exceeds the respective threshold. Asdescribed, in some embodiments, if one of the metrics exceeds thethreshold, then damage is detected. In other embodiments, two or threemetrics exceeding the threshold indicate that damage is detected.

FIG. 13 is a conceptual diagram illustrating the various windows in aninput image where damage is detected, according to one embodiment of thedisclosure. Now that the several indicators of damage within each windowhave been aggregated, for each vehicle part in the image found duringthe segmentation step, embodiments of the disclosure compute whether thefraction of “damaged” windows to the total number of windows coveringthe part exceeds a threshold. If it does, the whole part is declared asdamaged. Otherwise, it is not damaged. The “damaged” windows arethemselves combined together within their outer boundaries, which can bedisplayed to show the location of damage within each part, as shown inFIG. 13. The fraction of damaged area can be regarded as an indicator ofthe severity of damage to the part.

In addition to these “local” measures of damage, some embodiments canalso compute the overall shape of each vehicle part in the two imagesusing a shape descriptor, e.g., medial axis, and regard significantdifference between the two as further evidence of damage, which can becombined in a weighted manner with the preceding indicators to arrive atthe final estimate.

Referring back to FIG. 4, once external damage is detected at step 404,internal damage can be inferred at step 406. Since there is no directevidence of damage to internal parts from images of the damaged vehicle,embodiments of the disclosure infer damage to internal parts from damageto the external parts. In one implementation, pattern mining largeamounts of data of past auto claims can be used to infer damage to theinternal parts.

Some embodiments take a large number (e.g., on the order of thousands)of auto claims that contains images of the damaged vehicles and thecorresponding appraisals of damaged parts, as found by auto repair shopsfor repair purposes. Taken together, these historical claims provideenough evidence to establish a high degree of correlation between damagevisible in the images and the entire list of damaged parts, bothinternal and external. In one embodiment, a Convolutional Neural Network(CNN) is trained to learn this correlation. A CNN is a type ofmathematical device called a neural network that can be gradually tunedto learn the patterns of correlation between its input and output frombeing presented a large number of exemplars of input/output pairs calledtraining data. CNNs are configured to take into account the localstructure of visual images and invariance properties of objects that arepresent in them. CNNs have been shown to be highly effective at the taskof recognition of objects and their features provided there are enoughexemplars of all possible types in the data used to train them. Someembodiments train a CNN to output a complete list of damaged parts whenpresented with the set of images associated to an auto claim. Thisincludes both internal and external parts. The performance of the CNNcan be made more robust when it is presented with the output of theexternal damage detection system described above. The output of theexternal damage detection system “primes” the CNN with the informationabout which external parts are more likely to be damaged, and thereby,increases its accuracy and speed of convergence to the solution.

After both external and internal damaged parts are identified, thesystem can calculate an estimated repair cost at step 408. To arrive atthe estimated cost of parts and labor needed for repairing the vehicle,some embodiments provide the damaged parts list to a database of partsand labor costs. Several such databases exist and are already used byauto repair shops and insurance adjustors on a daily basis once a partslist is identified.

FIGS. 14-21 are screenshots of example interface screens of a vehicleclaims application on a client device, according to various embodimentsof the disclosure. As described, a vehicle claims application, such asvehicle claims application 218 in FIG. 2, may be used to capture imagesof a damaged vehicle and upload them to a server for processing.

FIG. 14 shows an example log-in screen of vehicle claims application.Once the user is authenticated, a home screen may be displayed, as shownin FIG. 15. Various links can be provided on the home screen to initiatea new claim 1502, review current policies 1504, review prior claims (“MyClaims”), find nearby repair shops, view emergency contacts, view theuser's profile, and view information about an insurance company (“AboutUs”). Selecting the current policies 1504 link may display policyinformation, as shown in FIG. 16.

If the user selects the new claim 1502 link, the interface in FIG. 17may be shown. If there are multiple vehicles insured, the user is askedto select which vehicle to which the new claim relates. Once a vehicleis selected, the interface in FIG. 18 may be displayed, where the useris prompted to take photos of the damaged parts of the vehicle. Thevehicle claims application may prompt the user for certain photos usinga three-dimensional (3d) model 1802, a parts list 1804, and vehicleviews 1806.

If the user selects to be prompted by a 3d model 1802, the interface inFIG. 19A may be displayed. A 3d model of the user's vehicle is displayedand the user is prompted to tap on the portion of the vehicle that isdamaged. For example, the user may tap on the hood of the vehicle, whichcauses an interface such as the one shown in FIG. 19B to be displayed.If the user selects “Yes” in the prompt in FIG. 19B, the interface inFIG. 19C may be displayed. In FIG. 19C, an outline 1902 is displayed forthe hood of the vehicle superimposed on a live camera view from theclient device. The user can then position the camera of the clientdevice so that the hood of the car aligns with the outline 1902. Oncethe hood of the car aligns with the outline 1902, a photo is captured,either automatically by the camera or manually by the user selecting acapture button. The user can be prompted in this manner to capturephotos of all damaged parts using a 3d model of the vehicle.

If instead the user selects to be prompted by a parts list 1804, theinterface in FIG. 20A may be displayed. The user is first prompted toselect general section of the vehicle that sustained damage. Suppose theuser select “Front” from the interface shown in FIG. 20A, which causesthe interface shown in FIG. 20B to be displayed. The user is thenprompted to select a more specific section or part of the vehicle thatsustained damage. Once the user makes a selection, an outline for thatpart is displayed (similar to the outline 1902 in FIG. 19C), and theclient device proceeds to capture the requisite photo.

If instead the user selects to be prompted by vehicle views 1806, theinterface in FIG. 21 may be displayed. The user is prompted to capturephotos of eight views of the vehicle, for example: front-leftperspective, front plan, front-right perspective, left plan, right plan,rear-left perspective, rear plan, rear-right perspective. In otherimplementations, different views may be requested.

Once the user captures the images of the damaged vehicle using theprompts provided by the vehicle claims application, the images areuploaded to a server over a network. The server is then configured toperform image processing operations on the images to identify damagedexternal parts, infer damaged internal parts, and estimate repair costs,as described above.

FIG. 22 is a screenshot of an example interface screen of an adjustercomputing device connected via a communications interface to a serverconfigured to automatically estimate repair costs, according to oneembodiment of the disclosure. In FIG. 22, in portion 2200 of theinterface, the original images uploaded to the server are shown. In thisexample, three images have been received by the server. Each of thethree images is processes separately. In portion 2202, the imagecurrently being processed is displayed. In portion 2204, the image afterbackground and specular reflection removal is shown. In portion 2206,the clean reference image is shown aligned with the image shown inportion 2204. Using the techniques described herein, the image shown inportion 2204 is compared to the image shown in portion 2206 to identifythe external parts that are damaged, from which internal parts areinferred, and total repair costs are estimated.

In some embodiments, in order to assist the adjustors to make decisionsquickly and easily using the output of the disclosed automated system,damaged area in each input image are marked in a contrasting color.Also, a label can be put onto the damaged part. Some embodiments thenproject the images onto the 3D model of the vehicle using the cameraangles determined during the alignment process. The 3D model then showsthe damage to the vehicle in an integrated manner. The adjustor canrotate and zoon in on the 3D model as desired. When the adjustor clickson a damaged part, the interface may show all the original images thatcontain that part on the side, so that the adjustor can easily examinein the original images where the damage was identified.

FIG. 23 is a flow diagram of method steps for a vehicle claimsapplication to prompt a client device to capture images of a damagedvehicle, according to one embodiment of the disclosure. At step 2302,the vehicle claims application receives a selection to initiate a newclaim. At step 2304, the vehicle claims application

At step 2304, the vehicle claims application receives a selection of aprompting interface for capture of images of damaged vehicle.

If the prompting interface is to capture images using a 3D model of thevehicle, at step 2306, the vehicle claims application displays a 3Dmodel of the vehicle. At step 2308, the vehicle claims applicationreceives a selection of a damaged part on the 3D model. At step 2310,the vehicle claims application displays an outline of the selected partfor a user to capture with a camera of the client device.

If the prompting interface is to capture images using a parts list ofthe vehicle, at step 2312, the vehicle claims application displays aparts list. At step 2314, the vehicle claims application receives aselection of part and, at step 2316, displays an outline of the part forthe user to capture with the camera of the client device.

If the prompting interface is to capture images using vehicle views, atstep 2318, the vehicle claims application displays two or more vehicleviews and, at step 2320, displays an outline for each vehicle view tocapture with the camera of the client device.

At step 2322, the vehicle claims application capture images of damage tovehicle using the camera of the client device. At step 2324, the vehicleclaims application uploads the captured images to a server for automaticestimation of repair costs.

In another implementation of the automatic vehicle damage assessment(AVDA) system, rather than comparing photos of a damaged vehicle to anundamaged version, another embodiment of the disclosure relies uponmachine learning methods to learn patterns of vehicle damage from alarge number of auto claims in order to predict damage for a new claim.In general, machine learning systems are systems that use “trainingdata” to “learn” to associate their input with a desired output.Learning is done by changing parameters of the system until the systemoutputs results as close to the desired outputs as possible. Once such amachine system has learned the input-output relationship from thetraining data, the machine learning system can be used to predict theoutput upon receiving a new input for which the output may not be known.The larger the training data set and the more representative of theinput space, the better the machine learning system performs on theprediction task.

Some embodiments use machine learning to perform the task of predictionof vehicle damage from an auto claim. Thousands of historical autoclaims are stored in one or more databases, such as database 110 in FIG.1, for training and testing of the disclosed system. The database alsostored auto claim images and other pieces of information that come witha claim, such as vehicle make, model, color, age, and current marketvalue, for example. The desired output of the disclosed machine learningsystem is the damage appraisal as prepared by a repair shop consistingof a list of parts that were repaired or replaced and the correspondingcosts of repair, both for parts and labor. Another desired output is thedetermination of the loss type, namely, total loss, medium loss, orsmall loss, for example.

FIG. 24 is a block diagram illustrating a multi-stage design of amachine learning system, according to one embodiment. At stage 2402,claims data for thousands of auto claims is input into the machinelearning system. The machine learning system is executed by one or morecomputing devices, such as servers 102 in FIG. 1.

At stage 2404, the machine learning system uses a machine learningmethod called Convolutional Neural Network (CNN) to detect externaldamage. A CNN is a type of machine learning method called an artificialneural network. A CNN is specially designed for image inputs based onanalogy with the human visual system. A CNN consists of a number oflayers of “neurons” or “feature maps,” also called convolution layers,followed by a number of layers called fully connected layers. The outputof a feature map is called a feature. In the convolution layers, the CNNextracts the essential aspects of an image in a progressivelyhierarchical fashion (i.e., from simple to complex) by combinatoriallycombining features from the previous layer in the next layer through aweighted non-linear function. In the fully connected layers, the CNNthen associates the most complex features of the image computed by thelast convolution layer with any desired output type, e.g., a damagedparts list, by outputting a non-linear weighted function of thefeatures. The various weights are adjusted during training, by comparingthe actual output of the network with the desired output and using ameasure of their difference (“loss function”) to calculate the amount ofchange in weights using the well-known backpropagation algorithm.Additional implementation details of the CNNs of the disclosed machinelearning system are described in detail below.

At stage 2406, the machine learning system predicts damage to theinterior parts of the vehicle from the exterior damage assessment outputby stage 2404. Some embodiments employ a Markov Random Field (MRF). AnMRF defines a joint probability distribution over a number of randomvariables whose mutual dependence structure is captured by an undirected(mathematical) graph. The graph includes one node for each randomvariable. If two nodes are connected by an edge, then the correspondingrandom variables are mutually dependent. The MRF joint distribution canbe written as a product of factors, one each of a maximal clique (i.e.,a maximal fully connected subgraph) in the graph. Additionalimplementations details of an MRF of the disclosed machine learningsystem are described in detail below.

At stage 2408, after the list of both exterior and interior damagedparts has been prepared, the machine learning system prepares a repaircost appraisal for the vehicle by looking up the damaged parts and laborcost in a database. The damaged parts list can be compared to a list ofpreviously damaged parts prior to the occurrence of the current damage,and a final list of newly damaged parts is determined throughsubtraction of previously damaged parts. Some embodiments also take intoaccount the geographical location, age of the vehicle, and otherfactors.

Additionally, some embodiments can classify a claim into categories as atotal, medium, or small loss claim by taking the damaged parts list,repair cost estimation, and current age and monetary value of thevehicle as input to a classifier whose output is the loss type whichtakes the three values—total, medium and small. Any machine learningtechnique can be used for the classifier, e.g., logistic regression,decision tree, artificial neural network, support vector machines (SVM),and bagging. First, the system is trained on historical claims for whichthe outcome is known. Once the system parameters have been to achieve adesired degree of accuracy on a test set, the system can be used toperform the loss classification.

CNN Implementation

As described, a CNN is a type of machine learning method called anartificial neural network. A CNN consists of a number of layers of“neurons” or “feature maps,” also called convolution layers, followed bya number of layers called fully connected layers. The output of afeature map is called a feature. In the convolution layers, the CNNextracts the essential aspects of an image in a progressivelyhierarchical fashion (i.e., from simple to complex) by combinatoriallycombining features from the previous layer in the next layer through aweighted non-linear function. In the fully connected layers, the CNNthen associates the most complex features of the image computed by thelast convolution layer with any desired output type, e.g., a damagedparts list, by outputting a non-linear weighted function of thefeatures. The various weights are adjusted during training, by comparingthe actual output of the network with the desired output and using ameasure of their difference (“loss function”) to calculate the amount ofchange in weights using the well-known backpropagation algorithm.

A “loss function” quantifies how far a current output of the CNN is fromthe desired output. The CNNs in some of the disclosed embodimentsperform classification tasks. In other words, the desired output is oneof several classes (e.g., damaged vs. non-damaged for a vehicle part).The output of the network is interpreted as a probability distributionover the classes. In implementation, the CNN can use a categoricalcross-entropy function to measure the loss using the following equation:H(p,q)=−Σ_(x) p(x) log(q(x))where p is a true distribution over classes for a given input x, and qis the output from the CNN for input x. The loss will be small if p andq are close to each other.In a first example, if we do positive and negative classification, andq=[0.1 0.9] and p=[0 1], then H₁=0.1. In a second example, if we dopositive and negative classification, and q=[0.9 0.1] and p=[0 1], thenH₂=2.3.

As described, a CNN is made up of layers. Each layer includes many“nodes” or “neurons” or “feature maps.” Each neuron has a simple task:it transforms its input to its output as a non-linear function, usuallya sigmoid or a rectified linear unit, of weighted linear combination ofits input. Some embodiments of the disclosure use a rectified linearunit. A CNN has four different types of layers:

-   -   1. “Input layer” that holds the raw pixel values of input        images.    -   2. “Convolutional layer” (Cony) that computes its output by        taking a small rectangular portion of its input (“window”) and        applying the non-linear weighted linear combination.    -   3. “Pooling layer” (Pool) that takes a rectangular portion of        its input (“window”) and computes either the maximum or average        of the input in that window. Embodiments of the disclosure use        the maximum operation. This layer reduces the input sizes by        combining many input elements into one.    -   4. “Fully connected layer” (FC), where each neuron in this layer        will be connected to all the numbers in the previous volume. The        output is a non-linear weighted linear combination of its input.

The parameters of a CNN are:

-   -   Number of layers    -   Number of neurons in each layer    -   Size of the window in each convolution and pooling layer    -   Weight vectors for each neuron in each layer    -   The parameters of the non-linearity used (the slope of the        rectified linear unit in our case)

Of these, the weight vectors for each neuron in each layer are the onesadjusted during training. The rest of the weight vectors, once chosen,remain fixed. For example, Table 1 below provides an examples of thenumber of parameters of used in one implementation for detection ofdamage to the front bumper:

TABLE 1 CNN Representation parameters size Weights Input: [240 × 320 ×3] 0 Conv1-64 neurons [240 × 320 × 64] (5*5*5)*64 = 8000 Pool1 [120 ×160 × 64] 0 Conv2-64 neurons [120 × 160 × 64] (5*5*64)*64 = 102,400Pool2 [60 × 80 × 64] 0 Conv3-64 neurons [60 × 80 × 64] (5*5*64)*64 =102,400 Pool3 [30 × 40 × 64] 0 FC1-256 neurons [1 × 1 × 256]30*40*64*256 = 19,660,800 FC2-256 neurons [1 × 1 × 256] 256*256 = 65,536FC3-2 neurons [1 × 1 × 2] 256*10 = 2,560CNN Training

The weight parameters of a CNN can be adjusted during the training phaseusing a back-propagation algorithm as follows:

initialize the network weights with small random values  do   for eachimage x in the training set    prediction = compute the output of thenetwork, q(x); // forward    pass    actual = desired output of thenetwork, p(x);    compute loss = H(p,q) = −Σ_(x)p(x)log(q(x)) for thebatch;    compute Δw_(h) = derivative of loss with respect to weight w_hfor allweights from hidden layer to output layer; // backward pass    add Δw_(h) to the current weights to get new weights;  until loss onthe training set drops below a threshold  return the network as trained

FIG. 25 is a block diagram illustrating implementation of ConvolutionalNeural Networks (CNNs) to detect vehicle damage, according to oneembodiment. In one implementation of the system, labeled images areinput to a CNN and the damaged parts list as the desired output. Inanother implementation, the input-output association problem is brokendown into several sub-problems, each of which is easier for a machinelearning system than the full problem, as shown in FIG. 15.

Claims data 2502 for thousands or millions of auto claims is input intothe exterior damage detection engine 2506. For a given claim for whichvehicle damage is to be detected, the claims data is also passed to avehicle pose classification engine 2504.

The vehicle pose classification engine 2504 uses a CNN to first predictthe pose of the vehicle. The output of this CNN is one of eight (8) posecategories. For vehicles, the 8 categories may correspond to the eight(8) non-overlapping 45-degree sectors around the vehicle, i.e., front,left front corner, left side, back front corner, back, back rightcorner, right side, and right front sector. The CNN of the vehicle poseclassification engine 2504 can be trained on a large number of autoclaim images that have manually been labeled with the appropriate posecategory.

In the exterior damage detection engine 2506, in one implementation,there is one CNN for each of the exterior vehicle parts, trained topredict damage to that part. In one implementation, a vehicle is dividedup into twenty-four (24) exterior parts, and thus, twenty-four (24)vehicle part CNNs, including:

Pr1=‘Front Bumper’;

Pr2=‘Back Bumper’;

Pr3=‘Front Windshield’,

Pr4=‘Back Windshield’;

Pr5=‘Hood’;

Pr6=‘Car Top’;

Pr7=‘Front Grill’;

Pr8=‘Left Front Fender’;

Pr9=‘Left Front Headlight’;

Pr10=‘Left Side’;

Pr11=‘Left Back Headlight’;

Pr12=‘Left Front Window’;

Pr13=‘Left Back Window’;

Pr14=‘Left Front Door’;

Pr15=‘Left Back Door’;

Pr16=‘Right Front Fender’;

Pr17=‘Right Front Headlight’;

Pr18=‘Right Side’;

Pr19=‘Right Back Headlight’;

Pr20=‘Right Front Window’;

Pr21=‘Right Back Window’;

Pr22=‘Right Front Door’;

Pr23=‘Right Back Door’; and

Pr24=‘Trunk’.

These CNNs can be trained on the auto claims images 2502, which havebeen labeled with an indication of damage to each exterior part visiblein the images.

After the pose category has been predicted by the vehicle poseclassification engine 2504 for a given input image, the image ispresented to each of the external part CNNs of the exterior damagedetection engine 2506. In one implementation, each CNN of the exteriordamage detection engine 2506 corresponds to an external part that ispotentially visible from that pose. Thus, a part CNN sees only thoseimages at its input that can have the part present in that post. Thisreduces the burden on the vehicle part CNNs in the exterior damagedetection engine 2506, while increasing their accuracy since theyreceive only the images relevant to the given vehicle part CNN.

After all the images in a claim have been presented to the exteriordamage detection engine 2506, the machine learning system has aprediction for damage to each of the exterior parts that we can inferfrom the collection of images for the claim.

This information is passed from the exterior damage detection engine2506 to the interior damage engine 2508. The interior damage engine 2508predicts damage to the interior parts of the vehicle from the exteriordamage assessment output by the exterior damage detection engine 2506.One implementation employs a Markov Random Field (MRF) in the interiordamage engine 2508. An MRF defines a joint probability distribution overa number of random variables whose mutual dependence structure iscaptured by an undirected (mathematical) graph. The graph includes onenode for each random variable. If two nodes are connected by an edge,the corresponding random variables are mutually dependent. The MRF jointdistribution can be written as a product of factors, one each of amaximal clique (a maximal fully connected subgraph) in the graph.

In one implementation, there is one random variable for damage level ofeach of the vehicle parts. The nodes corresponding to a pair of partsare connected by an edge if they are neighboring parts, since damage toone is likely to result in damage to the other. A probabilitydistribution is defined on these random variables that specifies theprobability for each subset of the parts that that subset is damagedwhile its complement is not damaged.

From the output of the exterior damage detection engine 2506, we canassign values to the random variables corresponding to the exteriorparts. The values of the random variables corresponding to the interiorparts can then inferred by choosing values that result in maximum jointprobability for the exterior and interior damaged parts. The inferencecan be carried out using a belief propagation algorithm.

The joint probability distribution over all the random variables p(y|θ)can first be written as due to the Hammersley-Clifford theorem, asfollows:

${p\left( {y❘\theta} \right)} = {\frac{1}{Z(\theta)}\underset{c \in C}{\Pi}{\psi_{c}\left( {y_{c}❘\theta_{c}} \right)}}$

Here, c is a maximal clique and θ_(c) are some parameters associatedwith the maxical clique. The potential functions ψ_(c) are chosen asexponential functions of weighted linear combinations of the parametersθ_(c) as:log ψ_(c)(y _(c))

ϕ_(c)(y _(c))^(T)θ_(c)

In one implementation, ϕc is identity. During training, the parametersθ_(c) are adjusted as follows: for any given auto claim, values of thevariables yc corresponding to the exterior and interior parts areclamped at their true values. The values of the parameters θ_(c) arechosen to then maximize the probability p(y|θ). This is repeated overthe entire set of training images until values of θ_(c) settle down tomore or less fixed values. These final values are taken as the values ofthe parameters θ_(c) for prediction of damage to interior parts.

The MRF is used to predict damage to interior parts as follows: given anew claim the values of yc corresponding to the exterior parts are fixedat the outputs of the corresponding part CNNs. The values of yccorresponding to interior parts are then chosen to maximize theprobability p(y|θ). For any interior parts if yc exceeds a pre-definedthreshold, it is regarded as damaged. Otherwise it is regarded asundamaged.

The external an internal damage estimates are then passed to a costestimation engine 2510. The cost estimation engine 2510 can look up in adatabase the corresponding cost for repair or replacement of each of theexternal and internal parts based on make, model, year, and color of thevehicle. Some embodiments also take into account the geographic locationof the vehicle, as costs may vary by state or region.

Additionally, some embodiments can classify a claim into categories as atotal, medium, or small loss claim by taking the damaged parts list,repair cost estimation, and current age and monetary value of thevehicle as input to a classifier whose output is the loss type whichtakes the three values—total, medium and small. Any machine learningtechnique can be used for the classifier, e.g., logistic regression,decision tree, artificial neural network. First, the system is trainedon historical claims for which the outcome is known. Once the systemparameters have been to achieve a desired degree of accuracy on a testset, the system can be used to perform the loss classification.

FIG. 26 is a flow diagram of method steps for estimating vehicle damagefrom images of a damaged vehicle using a machine learning algorithm,according to one embodiment. As shown, the method 2600 begins at step2602, where a server computing device trains a first ConvolutionalNeural Network (CNN) to detect pose of a vehicle. At step 2604, theserver computing device trains a plurality of CNNs to detect damage to arespective plurality of external vehicle parts 2604. At step 2606, theserver computing device receives a set of images corresponding to a newclaim. At step 2608, the server computing device executes the first CNNto detect the pose of the vehicle in each of the images in the set ofimage. At step 2610, the server computing device executes the pluralityof CNNs to determine which external vehicle parts are damaged. At step2612, the server computing device executes a Markov Random Field (MRF)algorithm to infer damage to internal parts of the vehicle from thedamaged external vehicle parts. At step 2614, the server computingdevice estimates a repair cost based on the external and internaldamaged parts. Additionally, in some embodiments, at step 2614, theserver computing device may classify the loss as a total, medium, orsmall loss, as described above.

All references, including publications, patent applications, andpatents, cited herein are hereby incorporated by reference to the sameextent as if each reference were individually and specifically indicatedto be incorporated by reference and were set forth in its entiretyherein.

The use of the terms “a” and “an” and “the” and “at least one” andsimilar referents in the context of describing the invention (especiallyin the context of the following claims) are to be construed to coverboth the singular and the plural, unless otherwise indicated herein orclearly contradicted by context. The use of the term “at least one”followed by a list of one or more items (for example, “at least one of Aand B”) is to be construed to mean one item selected from the listeditems (A or B) or any combination of two or more of the listed items (Aand B), unless otherwise indicated herein or clearly contradicted bycontext. The terms “comprising,” “having,” “including,” and “containing”are to be construed as open-ended terms (i.e., meaning “including, butnot limited to,”) unless otherwise noted. Recitation of ranges of valuesherein are merely intended to serve as a shorthand method of referringindividually to each separate value falling within the range, unlessotherwise indicated herein, and each separate value is incorporated intothe specification as if it were individually recited herein. All methodsdescribed herein can be performed in any suitable order unless otherwiseindicated herein or otherwise clearly contradicted by context. The useof any and all examples, or exemplary language (e.g., “such as”)provided herein, is intended merely to better illuminate the inventionand does not pose a limitation on the scope of the invention unlessotherwise claimed. No language in the specification should be construedas indicating any non-claimed element as essential to the practice ofthe invention.

Preferred embodiments of this invention are described herein, includingthe best mode known to the inventors for carrying out the invention.Variations of those preferred embodiments may become apparent to thoseof ordinary skill in the art upon reading the foregoing description. Theinventors expect skilled artisans to employ such variations asappropriate, and the inventors intend for the invention to be practicedotherwise than as specifically described herein. Accordingly, thisinvention includes all modifications and equivalents of the subjectmatter recited in the claims appended hereto as permitted by applicablelaw. Moreover, any combination of the above-described elements in allpossible variations thereof is encompassed by the invention unlessotherwise indicated herein or otherwise clearly contradicted by context.

What is claimed is:
 1. A method, comprising: causing a displaying of anoutline of a selected damaged part of a damaged vehicle to be capturedwith a camera of a client device; causing the camera of the clientdevice to capture an image of the damaged vehicle based on thedisplaying of the outline of the selected damaged part; receiving, at aserver computing device over an electronic network and from the clientdevice, the image of the damaged vehicle; aligning the image to anundamaged version of the damaged vehicle; segmenting the image intovehicle parts; and detecting damage to a set of parts of the damagedvehicle by comparing portions of each vehicle part in the image tocorresponding portions of each vehicle part in the undamaged version ofthe damaged vehicle, wherein detecting damage to the set of partsincludes: comparing at least one of edge distribution, texturecomparison, and spatial correlation of portions of each vehicle part inthe image to corresponding portions of each vehicle part in theundamaged version of the damaged vehicle; determining whether at leastone of the edge distribution, the texture comparison, and the spatialcorrelation exceeds a respective threshold difference value, whereindamage is detected in a portion of a vehicle part in the image if atleast one of the edge distribution, the texture comparison, and thespatial correlation exceed the respective threshold difference value;detecting a pose of the damaged vehicle in the image; and determiningwhich external vehicle parts are damaged in the image; and calculatingan estimated repair cost for the damaged vehicle based on which externalvehicle parts are damaged based on accessing a parts database thatincludes repair costs.
 2. The method of claim 1, wherein the partsdatabase that includes repair costs includes estimates for parts andlabor for individual parts.
 3. The method of claim 1, further comprisingremoving artifacts from the image by: removing background material fromthe image; and removing specular reflection due to incident light on thedamaged vehicle shown in the image.
 4. The method of claim 1, whereindamage is detected in a portion of a vehicle part in the image if atleast two of the edge distribution, the texture comparison, and thespatial correlation exceed the respective threshold difference value. 5.The method of claim 1, wherein the detecting damage to the set of partsincludes comparing each of edge distribution, texture comparison, andspatial correlation of portions of each vehicle part in the image tocorresponding portions of each vehicle part in the undamaged version ofthe damaged vehicle.
 6. The method of claim 1, wherein the detecting thepose of the damaged vehicle in the image comprises: training a firstConvolutional Neural Networks (CNN) of a plurality of CNNs to detect thepose of a damaged vehicle in the image; and training each of theplurality of CNNs to detect damage on a respective vehicle part of aplurality of external vehicle parts; and executing the first CNN todetect the pose of the damaged vehicle in the image.
 7. The method ofclaim 1, wherein detecting damage to the set of parts further includesinferring damage to internal parts of the damaged vehicle from thedetermined damaged external vehicle parts; and wherein calculating theestimated repair cost for the damaged vehicle is further based on whichinternal vehicle parts are inferred to be damaged.
 8. The method ofclaim 7, wherein inferring damage to internal parts of the damagedvehicle from the determined damaged external vehicle parts comprisesexecuting a Markov Random Field (MRF) algorithm.
 9. A non-transitorycomputer-readable medium storing instructions that, when executed by aprocessor, cause a computing device to perform operations of: causing adisplaying of an outline of a selected damaged part of a damaged vehicleto be captured with a camera of a client device; causing the camera ofthe client device to capture an image of the damaged vehicle based onthe displaying of the outline of the selected damaged part; receiving,from the client device, the image of the damaged vehicle; aligning theimage to an undamaged version of the damaged vehicle; segmenting theimage into vehicle parts; and detecting damage to a set of parts of thedamaged vehicle by comparing portions of each vehicle part in the imageto corresponding portions of each vehicle part in the undamaged versionof the damaged vehicle, wherein detecting damage to the set of partsincludes: comparing at least one of edge distribution, texturecomparison, and spatial correlation of portions of each vehicle part inthe image to corresponding portions of each vehicle part in theundamaged version of the damaged vehicle; determining whether at leastone of the edge distribution, the texture comparison, and the spatialcorrelation exceeds a respective threshold difference value, whereindamage is detected in a portion of a vehicle part in the image if atleast one of the edge distribution, the texture comparison, and thespatial correlation exceed the respective threshold difference value;detecting a pose of the damaged vehicle in the image; and determiningwhich external vehicle parts are damaged in the image; and calculatingan estimated repair cost for the damaged vehicle based on which externalvehicle parts are damaged based on accessing a parts database thatincludes repair costs.
 10. The computer-readable medium of claim 9,wherein the parts database that includes repair costs includes estimatesfor parts and labor for individual parts.
 11. The computer-readablemedium of claim 9, further comprising removing artifacts from the imageby: removing background material from the image; and removing specularreflection due to incident light on the damaged vehicle shown in theimage.
 12. The computer-readable medium of claim 9, wherein damage isdetected in a portion of a vehicle part in the image if at least two ofthe edge distribution, the texture comparison, and the spatialcorrelation exceed the respective threshold difference value.
 13. Thecomputer-readable medium of claim 9, wherein the detecting damage to theset of parts includes comparing each of edge distribution, texturecomparison, and spatial correlation of portions of each vehicle part inthe image to corresponding portions of each vehicle part in theundamaged version of the damaged vehicle.
 14. The computer-readablemedium of claim 9, wherein the detecting the pose of the damaged vehiclein the image comprises: training a first Convolutional Neural Networks(CNN) of a plurality of CNNs to detect the pose of a damaged vehicle inthe image; and training each of the plurality of CNNs to detect damageon a respective vehicle part of a plurality of external vehicle parts;and executing the first CNN to detect the pose of the damaged vehicle inthe image.
 15. The computer-readable medium of claim 9, whereindetecting damage to the set of parts further includes inferring damageto internal parts of the damaged vehicle from the determined damagedexternal vehicle parts; and wherein calculating the estimated repaircost for the damaged vehicle is further based on which internal vehicleparts are inferred to be damaged.
 16. The computer-readable medium ofclaim 15, wherein the inferring damage to internal parts of the damagedvehicle from the determined damaged external vehicle parts comprisesexecuting a Markov Random Field (MRF) algorithm.
 17. A computing device,comprising: a processor; and a memory storing instructions that, whenexecuted by the processor, cause the computing device to performoperations of: causing a displaying of an outline of a selected damagedpart of a damaged vehicle to be captured with a camera of a clientdevice; causing the camera of the client device to capture an image ofthe damaged vehicle based on the displaying of the outline of theselected damaged part; receiving, from the client device, the image ofthe damaged vehicle; aligning the image to an undamaged version of thedamaged vehicle; segmenting the image into vehicle parts; and detectingdamage to a set of parts of the damaged vehicle by comparing portions ofeach vehicle part in the image to corresponding portions of each vehiclepart in the undamaged version of the damaged vehicle, wherein detectingdamage to the set of parts includes: comparing at least one of edgedistribution, texture comparison, and spatial correlation of portions ofeach vehicle part in the image to corresponding portions of each vehiclepart in the undamaged version of the damaged vehicle; determiningwhether at least one of the edge distribution, the texture comparison,and the spatial correlation exceeds a respective threshold differencevalue, wherein damage is detected in a portion of a vehicle part in theimage if at least one of the edge distribution, the texture comparison,and the spatial correlation exceed the respective threshold differencevalue; detecting a pose of the damaged vehicle in the image; anddetermining which external vehicle parts are damaged in the image; andcalculating an estimated repair cost for the damaged vehicle based onwhich external vehicle parts are damaged based on accessing a partsdatabase that includes repair costs.
 18. The computing device of claim17, wherein the detecting the pose of the damaged vehicle in the imagecomprises: training a first Convolutional Neural Networks (CNN) of aplurality of CNNs to detect the pose of a damaged vehicle in the image;and training each of the plurality of CNNs to detect damage on arespective vehicle part of a plurality of external vehicle parts; andexecuting the first CNN to detect the pose of the damaged vehicle in theimage.
 19. The computing device of claim 17, wherein detecting damage tothe set of parts further includes inferring damage to internal parts ofthe damaged vehicle from the determined damaged external vehicle parts;and wherein calculating the estimated repair cost for the damagedvehicle is further based on which internal vehicle parts are inferred tobe damaged.
 20. The computing device of claim 19, wherein the inferringdamage to internal parts of the vehicle from the determined damagedexternal vehicle parts comprises executing a Markov Random Field (MRF)algorithm.